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Configuration error
from pathlib import Path | |
import torch | |
from peft import PeftModel | |
import modules.shared as shared | |
def add_lora_to_model(lora_names): | |
prior_set = set(shared.lora_names) | |
added_set = set(lora_names) - prior_set | |
removed_set = prior_set - set(lora_names) | |
shared.lora_names = list(lora_names) | |
# Nothing to do = skip. | |
if len(added_set) == 0 and len(removed_set) == 0: | |
return | |
# Only adding, and already peft? Do it the easy way. | |
if len(removed_set) == 0 and len(prior_set) > 0: | |
print(f"Adding the LoRA(s) named {added_set} to the model...") | |
for lora in added_set: | |
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) | |
return | |
# If removing anything, disable all and re-add. | |
if len(removed_set) > 0: | |
shared.model.disable_adapter() | |
if len(lora_names) > 0: | |
print("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names))) | |
params = {} | |
if not shared.args.cpu: | |
params['dtype'] = shared.model.dtype | |
if hasattr(shared.model, "hf_device_map"): | |
params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()} | |
elif shared.args.load_in_8bit: | |
params['device_map'] = {'': 0} | |
shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params) | |
for lora in lora_names[1:]: | |
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) | |
if not shared.args.load_in_8bit and not shared.args.cpu: | |
if not shared.args.monkey_patch: | |
shared.model.half() | |
if not hasattr(shared.model, "hf_device_map"): | |
if torch.has_mps: | |
device = torch.device('mps') | |
shared.model = shared.model.to(device) | |
else: | |
shared.model = shared.model.cuda() | |